# From Speech to Text Corpora: Evaluating ASR-Based Data Acquisition for Low-Resource Fongbe and Hausa

arXiv: https://arxiv.org/abs/2606.22274 · HTML: https://mlsanity.com/paper/2606.22274/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 22 references that are in the map.
- First posted on arXiv 20 Jun 2026.

## Builds on (most cited first, 12 of 12)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [Scaling Speech Technology to 1,000+ Languages](https://mlsanity.com/paper/2305.13516/) (2023)
- [MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition](https://mlsanity.com/paper/2210.12391/) (2022)
- [MasakhaPOS: Part-of-Speech Tagging for Typologically Diverse African Languages](https://mlsanity.com/paper/2305.13989/) (2023)
- [InkubaLM: A small language model for low-resource African languages](https://mlsanity.com/paper/2408.17024/) (2024)
- [AfriHuBERT: A self-supervised speech representation model for African languages](https://mlsanity.com/paper/2409.20201/) (2024)
- [The NaijaVoices Dataset: Cultivating Large-Scale, High-Quality, Culturally-Rich Speech Data for African Languages](https://mlsanity.com/paper/2505.20564/) (2025)
- [Automatic Speech Recognition for African Low-Resource Languages: Challenges and Future Directions](https://mlsanity.com/paper/2505.11690/) (2025)
- [FFSTC: Fongbe to French Speech Translation Corpus](https://mlsanity.com/paper/2403.05488/) (2024)
- [Automatic Speech Recognition (ASR) for African Low-Resource Languages: A Systematic Literature Review](https://mlsanity.com/paper/2510.01145/) (2025)

## Abstract (arXiv)

Low-resource African languages lack text corpora needed for language model training. We investigate whether ASR pipelines can extend text resources for two typologically distinct West African languages: Fongbe (tonal, diacritic-rich) and Hausa (non-tonal). We fine-tune MMS-300M on a curated 12.3-hour Fongbe dataset, achieving 9.48% WER on the ALFFA benchmark - a 78% relative reduction from the prior 44.04% baseline - while preserving tonal diacritics critical to the language. For Hausa, we apply an existing fine-tuned Whisper-Small model. We catalog 1,553 YouTube videos (236 hours) and process a subset of 424 videos (45.49 hours) selected to balance domain diversity with available computational resources, producing 6,770 transcribed segments. Human evaluation on 50 randomly sampled segments per language shows mean quality scores of 57.4/100 for Hausa and 36.5/100 for Fongbe, indicating that while Hausa transcriptions approach acceptable quality for corpus construction, Fongbe transcriptions require post-processing or improved models for production use. We release the curated dataset, fine-tuned model, transcribed corpus, and full video catalog following platform terms and ethical guidelines.